autonomousvision/occupancy_networks

Health v2 · maintenance only

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1663 stars · 303 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of the CVPR 2019 paper 'Occupancy Networks: Learning 3D Reconstruction in Function Space'. It learns continuous 3D occupancy functions to reconstruct 3D meshes from single images, including pretrained models and training/evaluation pipelines on ShapeNet.

Use cases

• reconstruct 3D meshes from single images
• train occupancy network models on ShapeNet
• evaluate 3D reconstruction models
• generate meshes from demo input images
• research implicit 3D shape representations

When to choose

• you need single-view 3D reconstruction with occupancy-based implicit representations
• you want to reproduce or build on the Occupancy Networks paper
• you need pretrained models and preprocessing pipelines for ShapeNet

When to avoid

• you need real-time 3D reconstruction on low-end hardware without a CUDA GPU
• you want a production-ready 3D reconstruction service rather than research code
• you need recent state-of-the-art neural reconstruction methods

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning graphics python 3d-reconstruction occupancy-networks shapenet mesh-generation cvpr-2019 research-code pytorch linux gpu

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For agents

markdown · JSON · MCP: product_card(name="autonomousvision/occupancy_networks")